Code
library(here)
library(readr)
library(janitor)
library(dplyr)
library(magrittr)
library(likert)
library(ggplot2)
library(tidyr)
# wordcloud
library(tidytext)
library(wordcloud)library(here)
library(readr)
library(janitor)
library(dplyr)
library(magrittr)
library(likert)
library(ggplot2)
library(tidyr)
# wordcloud
library(tidytext)
library(wordcloud)data_location <- here("Data", "2024 - AMINGA Youth Participant Survey Responses - Master.csv")
survey <- read_csv(data_location, skip = 2) %>%
clean_names()
survey_df <- survey %>%
dplyr::select(
status,
gender_genero,
sport_overall_experience_1_not_great_3_neutral_5_amazing_experiencia_na_modalidade_1_suficiente_3_bom_5_muito_bom,
english_1_not_great_3_neutral_5_amazing_ingles_1_suficiente_3_bom_5_muito_bom,
art_1_not_great_3_neutral_5_amazing_arte_1_suficiente_3_bom_5_muito_bom,
computer_1_not_great_3_neutral_5_amazing_computer_1_suficiente_3_bom_5_muito_bom,
team_building_1_not_great_3_neutral_5_amazing_trabalho_de_equipa_1_suficiente_3_bom_5_muito_bom,
human_rights_direitos_humanos_1_not_great_3_neutral_5_amazing_direitos_humanos_1_suficiente_3_bom_5_muito_bom,
career_readiness_orientacao_de_carreira_1_not_great_3_neutral_5_amazing_orientacao_de_carreira_1_suficiente_3_bom_5_muito_bom,
yoga_mindfulness_1_not_great_3_neutral_5_amazing_ioga_atencao_plena_1_suficiente_3_bom_5_muito_bom,
food_1_not_great_3_neutral_5_amazing_comida_1_suficiente_3_bom_5_muito_bom,
aminga_staff_2024_overall_1_not_great_3_neutral_5_amazing_equipa_de_aminga_2024_em_geral_1_suficiente_3_bom_5_muito_bom,
aminga_camp_2024_overall_1_not_great_3_neutral_5_amazing_campus_aminga_2024_em_geral_1_suficiente_3_bom_5_muito_bom,
do_you_want_to_return_to_aminga_camp_in_2024_queres_voltar_para_o_campus_aminga_em_2024
) %>%
dplyr::rename(
english = english_1_not_great_3_neutral_5_amazing_ingles_1_suficiente_3_bom_5_muito_bom,
art = art_1_not_great_3_neutral_5_amazing_arte_1_suficiente_3_bom_5_muito_bom,
computer = computer_1_not_great_3_neutral_5_amazing_computer_1_suficiente_3_bom_5_muito_bom,
team_building = team_building_1_not_great_3_neutral_5_amazing_trabalho_de_equipa_1_suficiente_3_bom_5_muito_bom,
human_rights = human_rights_direitos_humanos_1_not_great_3_neutral_5_amazing_direitos_humanos_1_suficiente_3_bom_5_muito_bom,
career_readiness = career_readiness_orientacao_de_carreira_1_not_great_3_neutral_5_amazing_orientacao_de_carreira_1_suficiente_3_bom_5_muito_bom,
yoga = yoga_mindfulness_1_not_great_3_neutral_5_amazing_ioga_atencao_plena_1_suficiente_3_bom_5_muito_bom,
food = food_1_not_great_3_neutral_5_amazing_comida_1_suficiente_3_bom_5_muito_bom,
staff_overall = aminga_staff_2024_overall_1_not_great_3_neutral_5_amazing_equipa_de_aminga_2024_em_geral_1_suficiente_3_bom_5_muito_bom,
camp_overall = aminga_camp_2024_overall_1_not_great_3_neutral_5_amazing_campus_aminga_2024_em_geral_1_suficiente_3_bom_5_muito_bom,
sport = status,
gender = gender_genero,
sport_experience = sport_overall_experience_1_not_great_3_neutral_5_amazing_experiencia_na_modalidade_1_suficiente_3_bom_5_muito_bom,
return_next_year = do_you_want_to_return_to_aminga_camp_in_2024_queres_voltar_para_o_campus_aminga_em_2024
) %>%
mutate(
sport = trimws(gsub("\\([^\\(\\)]*\\)", "", sport)),
gender = trimws(gsub("\\([^\\(\\)]*\\)", "", gender)),
return_next_year = trimws(gsub("\\s*\\([^\\)]+\\)","",return_next_year))
) %>%
mutate(across(where(is.numeric), function(x) factor(x, levels = 1:5, labels = c(
"Not Great",
"Meh",
"Neutral",
"Great",
"Amazing"
)))) %>%
as.data.frame()
comments_sport_df <- survey %>%
select(
i_loved_about_the_sport_eu_amei_sobre_a_modalidade,
i_would_change_about_the_sport_eu_mudaria_sobre_a_modalidade,
comments_regarding_art_class_comentarios_sobre_as_aulas_de_arte,
comments_regarding_english_class_comentarios_sobre_a_aula_de_ingles,
comments_regarding_computer_class_comentarios_sobre_as_aulas_de_informatica,
comments_regarding_team_building_comentarios_sobre_trabalho_de_equipa,
comments_regarding_human_rights_comentarios_sobre_direitos_humanos,
comments_regarding_career_readiness_comentarios_sobre_orientacao_de_carreira,
comments_regarding_yoga_class_comentarios_sobre_a_aula_de_yoga,
comments_regarding_the_food_comentarios_sobre_a_comida,
aminga_staff_2024_overall_insert_appreciations_for_any_of_the_staff_equipa_de_aminga_em_geral_algum_comentario_sobre_qualquer_um_da_equipa_aminga,
any_comments_or_suggestions_algum_comentario_ou_sugestao
)
prep_word_cloud <- function(colname){
pt_stopwords <- tibble(word = stopwords::stopwords('pt'))
comments_sport_df %>%
select({{colname}}) %>%
unnest_tokens(word, {{colname}}) %>%
count(word, sort = TRUE)%>%
anti_join(pt_stopwords, by = "word")
}likert_overall_sport <- likert(items = survey_df %>% dplyr::select(sport_experience))
likert_overall_sport Item Not Great Meh Neutral Great Amazing
1 sport_experience 0 0 17.94872 11.53846 70.51282
We can see that over 70% of students thought that the camp was amazing.
plot(likert_overall_sport)We can see that over 82% of students thought that the camp was either great or amazing.
likert_subjects <- likert(items = survey_df %>% dplyr::select(art, english, computer, human_rights, career_readiness, team_building, yoga))likert_subjects$results| Item | Not Great | Meh | Neutral | Great | Amazing |
|---|---|---|---|---|---|
| art | 1.282051 | 0.000000 | 19.230769 | 5.128205 | 74.35897 |
| english | 2.597403 | 3.896104 | 33.766234 | 11.688312 | 48.05195 |
| computer | 2.564103 | 1.282051 | 25.641026 | 10.256410 | 60.25641 |
| human_rights | 0.000000 | 0.000000 | 20.779221 | 11.688312 | 67.53247 |
| career_readiness | 8.860760 | 5.063291 | 29.113924 | 16.455696 | 40.50633 |
| team_building | 1.282051 | 0.000000 | 1.282051 | 1.282051 | 96.15385 |
| yoga | 2.666667 | 8.000000 | 10.666667 | 9.333333 | 69.33333 |
plot(likert_subjects, centered=TRUE, center=3, include.center=TRUE)We can see that over 97% of students thought that team building was either great or amazing. In contrast, 57% of students thought that career readiness was either great or amazing.
likert_overall_food <- likert(items = survey_df %>% dplyr::select(food))likert_overall_food$results| Item | Not Great | Meh | Neutral | Great | Amazing |
|---|---|---|---|---|---|
| food | 0 | 0 | 7.792208 | 3.896104 | 88.31169 |
We can see that over 88% of students thought that the food was amazing.
plot(likert_overall_food)likert_overall_staff <- likert(items = survey_df %>% dplyr::select(staff_overall))likert_overall_staff$results| Item | Not Great | Meh | Neutral | Great | Amazing |
|---|---|---|---|---|---|
| staff_overall | 0 | 0 | 3.846154 | 7.692308 | 88.46154 |
plot(likert_overall_staff)We can see that 96% of students thought that the staff was great or amazing. Furthermore, no students had a negative opinion of staff.
likert_overall_camp <- likert(items = survey_df %>% dplyr::select(camp_overall))likert_overall_camp$results| Item | Not Great | Meh | Neutral | Great | Amazing |
|---|---|---|---|---|---|
| camp_overall | 0 | 0 | 0 | 10.25641 | 89.74359 |
plot(likert_overall_camp)We can see that all of the students had a poitive experience of the camp.
The question originally was the following:
return_next_year_df <- survey_df %>%
dplyr::select(return_next_year) %>%
count(return_next_year) %>%
mutate(return_next_year = replace_na(return_next_year, "No Response"))
return_next_year_df| return_next_year | n |
|---|---|
| Maybe | 6 |
| Yes | 71 |
| No Response | 6 |
ggplot(return_next_year_df, aes(x = return_next_year, y = n)) +
geom_col()We can see that over 70 students want to return to the camp next year.
temp_class_gender <- survey_df %>%
dplyr::select(art, english, computer, human_rights, career_readiness, team_building, yoga, gender) %>%
drop_na()
likert_subjects_gender <- likert(
items = temp_class_gender %>% dplyr::select(art, english, computer, human_rights, career_readiness, team_building, yoga) ,
grouping = temp_class_gender %>% dplyr::pull(gender)
)likert_subjects_gender$results| Group | Item | Not Great | Meh | Neutral | Great | Amazing |
|---|---|---|---|---|---|---|
| Female | art | 3.125000 | 0.000000 | 15.62500 | 6.250000 | 75.00000 |
| Female | english | 6.250000 | 6.250000 | 37.50000 | 12.500000 | 37.50000 |
| Female | computer | 0.000000 | 0.000000 | 40.62500 | 9.375000 | 50.00000 |
| Female | human_rights | 0.000000 | 0.000000 | 25.00000 | 15.625000 | 59.37500 |
| Female | career_readiness | 9.375000 | 6.250000 | 31.25000 | 18.750000 | 34.37500 |
| Female | team_building | 3.125000 | 0.000000 | 3.12500 | 0.000000 | 93.75000 |
| Female | yoga | 6.250000 | 9.375000 | 6.25000 | 9.375000 | 68.75000 |
| Male | art | 0.000000 | 0.000000 | 11.11111 | 5.555556 | 83.33333 |
| Male | english | 0.000000 | 0.000000 | 27.77778 | 13.888889 | 58.33333 |
| Male | computer | 5.555556 | 0.000000 | 16.66667 | 8.333333 | 69.44444 |
| Male | human_rights | 0.000000 | 0.000000 | 22.22222 | 8.333333 | 69.44444 |
| Male | career_readiness | 2.777778 | 5.555556 | 27.77778 | 16.666667 | 47.22222 |
| Male | team_building | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 100.00000 |
| Male | yoga | 0.000000 | 2.777778 | 11.11111 | 11.111111 | 75.00000 |
plot(likert_subjects_gender)temp_food_gender <- survey_df %>% dplyr::select(gender, food) %>% drop_na()
likert_food_gender <- likert(temp_food_gender %>% select(food), grouping = temp_food_gender %>% pull(gender))
likert_food_gender$results| Group | Item | Not Great | Meh | Neutral | Great | Amazing |
|---|---|---|---|---|---|---|
| Female | food | 0 | 0 | 8.823529 | 2.941177 | 88.23529 |
| Male | food | 0 | 0 | 4.761905 | 4.761905 | 90.47619 |
summary(likert_food_gender, center = 3, ordered = TRUE)| Group | Item | low | neutral | high | mean | sd |
|---|---|---|---|---|---|---|
| Female | food | 0 | 8.823529 | 91.17647 | 4.794118 | 0.5918339 |
| Male | food | 0 | 4.761905 | 95.23810 | 4.857143 | 0.4722251 |
plot(likert_food_gender , centered=TRUE, center=3, include.center=TRUE)temp_camp_gender <- survey_df %>% dplyr::select(gender, staff_overall, camp_overall) %>% drop_na()
likert_camp_gender <- likert(temp_camp_gender %>% select(staff_overall, camp_overall), grouping = temp_camp_gender %>% pull(gender))
likert_camp_gender$results| Group | Item | Not Great | Meh | Neutral | Great | Amazing |
|---|---|---|---|---|---|---|
| Female | staff_overall | 0 | 0 | 2.857143 | 5.714286 | 91.42857 |
| Female | camp_overall | 0 | 0 | 0.000000 | 17.142857 | 82.85714 |
| Male | staff_overall | 0 | 0 | 4.761905 | 9.523810 | 85.71429 |
| Male | camp_overall | 0 | 0 | 0.000000 | 4.761905 | 95.23810 |
summary(likert_camp_gender, center = 3, ordered = TRUE)| Group | Item | low | neutral | high | mean | sd |
|---|---|---|---|---|---|---|
| Female | staff_overall | 0 | 2.857143 | 97.14286 | 4.885714 | 0.4037638 |
| Female | camp_overall | 0 | 0.000000 | 100.00000 | 4.828571 | 0.3823853 |
| Male | staff_overall | 0 | 4.761905 | 95.23810 | 4.809524 | 0.5054867 |
| Male | camp_overall | 0 | 0.000000 | 100.00000 | 4.952381 | 0.2155403 |
plot(likert_camp_gender, centered=TRUE, center=3, include.center=TRUE)temp_class_sport <- survey_df %>%
dplyr::select(art, english, computer, human_rights, career_readiness, team_building, yoga, sport) %>%
drop_na()
likert_subjects_sport <- likert(
items = temp_class_gender %>% dplyr::select(art, english, computer, human_rights, career_readiness, team_building, yoga) ,
grouping = temp_class_sport %>% dplyr::pull(sport)
)
likert_subjects_sport$results| Group | Item | Not Great | Meh | Neutral | Great | Amazing |
|---|---|---|---|---|---|---|
| Basketball | art | 0.000000 | 0 | 5.555556 | 16.66667 | 77.77778 |
| Basketball | english | 0.000000 | 0 | 33.333333 | 22.22222 | 44.44444 |
| Basketball | computer | 5.555556 | 0 | 16.666667 | 16.66667 | 61.11111 |
| Basketball | human_rights | 0.000000 | 0 | 22.222222 | 16.66667 | 61.11111 |
| Basketball | career_readiness | 0.000000 | 0 | 22.222222 | 22.22222 | 55.55556 |
| Basketball | team_building | 0.000000 | 0 | 0.000000 | 0.00000 | 100.00000 |
| Basketball | yoga | 0.000000 | 0 | 11.111111 | 11.11111 | 77.77778 |
| Handball | art | 0.000000 | 0 | 16.000000 | 0.00000 | 84.00000 |
| Handball | english | 0.000000 | 8 | 40.000000 | 8.00000 | 44.00000 |
| Handball | computer | 0.000000 | 0 | 40.000000 | 4.00000 | 56.00000 |
| Handball | human_rights | 0.000000 | 0 | 32.000000 | 8.00000 | 60.00000 |
| Handball | career_readiness | 8.000000 | 4 | 36.000000 | 16.00000 | 36.00000 |
| Handball | team_building | 4.000000 | 0 | 4.000000 | 0.00000 | 92.00000 |
| Handball | yoga | 4.000000 | 4 | 8.000000 | 4.00000 | 80.00000 |
| Volleyball | art | 4.000000 | 0 | 16.000000 | 4.00000 | 76.00000 |
| Volleyball | english | 8.000000 | 0 | 24.000000 | 12.00000 | 56.00000 |
| Volleyball | computer | 4.000000 | 0 | 24.000000 | 8.00000 | 64.00000 |
| Volleyball | human_rights | 0.000000 | 0 | 16.000000 | 12.00000 | 72.00000 |
| Volleyball | career_readiness | 8.000000 | 12 | 28.000000 | 16.00000 | 36.00000 |
| Volleyball | team_building | 0.000000 | 0 | 0.000000 | 0.00000 | 100.00000 |
| Volleyball | yoga | 4.000000 | 12 | 8.000000 | 16.00000 | 60.00000 |
summary(likert_subjects_sport,center = 3, ordered = TRUE)| Group | Item | low | neutral | high | mean | sd |
|---|---|---|---|---|---|---|
| Basketball | art | 0.000000 | 5.555556 | 94.44444 | 4.722222 | 0.5745131 |
| Basketball | english | 0.000000 | 33.333333 | 66.66667 | 4.111111 | 0.9002541 |
| Basketball | computer | 5.555556 | 16.666667 | 77.77778 | 4.277778 | 1.1274936 |
| Basketball | human_rights | 0.000000 | 22.222222 | 77.77778 | 4.388889 | 0.8498366 |
| Basketball | career_readiness | 0.000000 | 22.222222 | 77.77778 | 4.333333 | 0.8401681 |
| Basketball | team_building | 0.000000 | 0.000000 | 100.00000 | 5.000000 | 0.0000000 |
| Basketball | yoga | 0.000000 | 11.111111 | 88.88889 | 4.666667 | 0.6859943 |
| Handball | art | 0.000000 | 16.000000 | 84.00000 | 4.680000 | 0.7483315 |
| Handball | english | 8.000000 | 40.000000 | 52.00000 | 3.880000 | 1.0923980 |
| Handball | computer | 0.000000 | 40.000000 | 60.00000 | 4.160000 | 0.9865766 |
| Handball | human_rights | 0.000000 | 32.000000 | 68.00000 | 4.280000 | 0.9363048 |
| Handball | career_readiness | 12.000000 | 36.000000 | 52.00000 | 3.680000 | 1.2489996 |
| Handball | team_building | 4.000000 | 4.000000 | 92.00000 | 4.760000 | 0.8793937 |
| Handball | yoga | 8.000000 | 8.000000 | 84.00000 | 4.520000 | 1.0847427 |
| Volleyball | art | 4.000000 | 16.000000 | 80.00000 | 4.480000 | 1.0456258 |
| Volleyball | english | 8.000000 | 24.000000 | 68.00000 | 4.080000 | 1.2556539 |
| Volleyball | computer | 4.000000 | 24.000000 | 72.00000 | 4.280000 | 1.1000000 |
| Volleyball | human_rights | 0.000000 | 16.000000 | 84.00000 | 4.560000 | 0.7681146 |
| Volleyball | career_readiness | 20.000000 | 28.000000 | 52.00000 | 3.600000 | 1.3228757 |
| Volleyball | team_building | 0.000000 | 0.000000 | 100.00000 | 5.000000 | 0.0000000 |
| Volleyball | yoga | 16.000000 | 8.000000 | 76.00000 | 4.160000 | 1.2476645 |
plot(likert_subjects_sport, centered=TRUE, center=3, include.center=TRUE)temp_food_sport <- survey_df %>% dplyr::select(sport, food) %>% drop_na()
likert_food_sport <- likert(temp_food_sport %>% select(food), grouping = temp_food_sport %>% pull(sport))
likert_food_sport$results| Group | Item | Not Great | Meh | Neutral | Great | Amazing |
|---|---|---|---|---|---|---|
| Basketball | food | 0 | 0 | 4.00000 | 4.000000 | 92.00000 |
| Handball | food | 0 | 0 | 4.00000 | 0.000000 | 96.00000 |
| Volleyball | food | 0 | 0 | 11.53846 | 7.692308 | 80.76923 |
summary(likert_food_sport, center = 3, ordered = TRUE)| Group | Item | low | neutral | high | mean | sd |
|---|---|---|---|---|---|---|
| Basketball | food | 0 | 4.00000 | 96.00000 | 4.880000 | 0.4396969 |
| Handball | food | 0 | 4.00000 | 96.00000 | 4.920000 | 0.4000000 |
| Volleyball | food | 0 | 11.53846 | 88.46154 | 4.692308 | 0.6793662 |
plot(likert_food_sport , centered=TRUE, center=3, include.center=TRUE)temp_overall_sport <- survey_df %>% dplyr::select(sport, staff_overall, camp_overall) %>% drop_na()
likert_camp_sport <- likert(temp_overall_sport %>% select(staff_overall, camp_overall), grouping = temp_overall_sport %>% pull(sport))
likert_camp_sport$results| Group | Item | Not Great | Meh | Neutral | Great | Amazing |
|---|---|---|---|---|---|---|
| Basketball | staff_overall | 0 | 0 | 4.000000 | 12.000000 | 84.00000 |
| Basketball | camp_overall | 0 | 0 | 0.000000 | 0.000000 | 100.00000 |
| Handball | staff_overall | 0 | 0 | 4.000000 | 8.000000 | 88.00000 |
| Handball | camp_overall | 0 | 0 | 0.000000 | 12.000000 | 88.00000 |
| Volleyball | staff_overall | 0 | 0 | 3.703704 | 3.703704 | 92.59259 |
| Volleyball | camp_overall | 0 | 0 | 0.000000 | 18.518518 | 81.48148 |
summary(likert_camp_sport, center = 3, ordered = TRUE)| Group | Item | low | neutral | high | mean | sd |
|---|---|---|---|---|---|---|
| Basketball | staff_overall | 0 | 4.000000 | 96.0000 | 4.800000 | 0.5000000 |
| Basketball | camp_overall | 0 | 0.000000 | 100.0000 | 5.000000 | 0.0000000 |
| Handball | staff_overall | 0 | 4.000000 | 96.0000 | 4.840000 | 0.4725816 |
| Handball | camp_overall | 0 | 0.000000 | 100.0000 | 4.880000 | 0.3316625 |
| Volleyball | staff_overall | 0 | 3.703704 | 96.2963 | 4.888889 | 0.4236593 |
| Volleyball | camp_overall | 0 | 0.000000 | 100.0000 | 4.814815 | 0.3958474 |
plot(likert_camp_sport, centered=TRUE, center=3, include.center=TRUE)temp_class_sport_gender<- survey_df %>% drop_na(gender, sport) %>% unite("sport group", c(gender, sport), sep = " ") %>% dplyr::select(`sport group`, art, english, computer, human_rights, career_readiness, team_building, yoga) %>% drop_na()
likert_subjects_sport_gender <- likert(
items = temp_class_sport_gender %>% dplyr::select(english, art, computer, team_building, yoga) ,
grouping = temp_class_sport_gender %>% dplyr::pull(`sport group`)
)
likert_subjects_sport_gender$results| Group | Item | Not Great | Meh | Neutral | Great | Amazing |
|---|---|---|---|---|---|---|
| Female Basketball | english | 0.000000 | 0.000000 | 33.333333 | 50.000000 | 16.66667 |
| Female Basketball | art | 0.000000 | 0.000000 | 0.000000 | 33.333333 | 66.66667 |
| Female Basketball | computer | 0.000000 | 0.000000 | 33.333333 | 0.000000 | 66.66667 |
| Female Basketball | team_building | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 100.00000 |
| Female Basketball | yoga | 0.000000 | 0.000000 | 0.000000 | 16.666667 | 83.33333 |
| Female Handball | english | 0.000000 | 15.384615 | 46.153846 | 0.000000 | 38.46154 |
| Female Handball | art | 0.000000 | 0.000000 | 23.076923 | 0.000000 | 76.92308 |
| Female Handball | computer | 0.000000 | 0.000000 | 53.846154 | 7.692308 | 38.46154 |
| Female Handball | team_building | 7.692308 | 0.000000 | 7.692308 | 0.000000 | 84.61538 |
| Female Handball | yoga | 7.692308 | 7.692308 | 7.692308 | 7.692308 | 69.23077 |
| Female Volleyball | english | 15.384615 | 0.000000 | 30.769231 | 7.692308 | 46.15385 |
| Female Volleyball | art | 7.692308 | 0.000000 | 15.384615 | 0.000000 | 76.92308 |
| Female Volleyball | computer | 0.000000 | 0.000000 | 30.769231 | 15.384615 | 53.84615 |
| Female Volleyball | team_building | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 100.00000 |
| Female Volleyball | yoga | 7.692308 | 15.384615 | 7.692308 | 7.692308 | 61.53846 |
| Male Basketball | english | 0.000000 | 0.000000 | 33.333333 | 8.333333 | 58.33333 |
| Male Basketball | art | 0.000000 | 0.000000 | 8.333333 | 8.333333 | 83.33333 |
| Male Basketball | computer | 8.333333 | 0.000000 | 8.333333 | 25.000000 | 58.33333 |
| Male Basketball | team_building | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 100.00000 |
| Male Basketball | yoga | 0.000000 | 0.000000 | 16.666667 | 8.333333 | 75.00000 |
| Male Handball | english | 0.000000 | 0.000000 | 33.333333 | 16.666667 | 50.00000 |
| Male Handball | art | 0.000000 | 0.000000 | 8.333333 | 0.000000 | 91.66667 |
| Male Handball | computer | 0.000000 | 0.000000 | 25.000000 | 0.000000 | 75.00000 |
| Male Handball | team_building | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 100.00000 |
| Male Handball | yoga | 0.000000 | 0.000000 | 8.333333 | 0.000000 | 91.66667 |
| Male Volleyball | english | 0.000000 | 0.000000 | 16.666667 | 16.666667 | 66.66667 |
| Male Volleyball | art | 0.000000 | 0.000000 | 16.666667 | 8.333333 | 75.00000 |
| Male Volleyball | computer | 8.333333 | 0.000000 | 16.666667 | 0.000000 | 75.00000 |
| Male Volleyball | team_building | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 100.00000 |
| Male Volleyball | yoga | 0.000000 | 8.333333 | 8.333333 | 25.000000 | 58.33333 |
summary(likert_subjects_sport_gender,center = 3, ordered = TRUE)| Group | Item | low | neutral | high | mean | sd |
|---|---|---|---|---|---|---|
| Female Basketball | english | 0.000000 | 33.333333 | 66.66667 | 3.833333 | 0.7527727 |
| Female Basketball | art | 0.000000 | 0.000000 | 100.00000 | 4.666667 | 0.5163978 |
| Female Basketball | computer | 0.000000 | 33.333333 | 66.66667 | 4.333333 | 1.0327956 |
| Female Basketball | team_building | 0.000000 | 0.000000 | 100.00000 | 5.000000 | 0.0000000 |
| Female Basketball | yoga | 0.000000 | 0.000000 | 100.00000 | 4.833333 | 0.4082483 |
| Female Handball | english | 15.384615 | 46.153846 | 38.46154 | 3.615385 | 1.1929279 |
| Female Handball | art | 0.000000 | 23.076923 | 76.92308 | 4.538462 | 0.8770580 |
| Female Handball | computer | 0.000000 | 53.846154 | 46.15385 | 3.846154 | 0.9870962 |
| Female Handball | team_building | 7.692308 | 7.692308 | 84.61538 | 4.538462 | 1.1982894 |
| Female Handball | yoga | 15.384615 | 7.692308 | 76.92308 | 4.230769 | 1.3634421 |
| Female Volleyball | english | 15.384615 | 30.769231 | 53.84615 | 3.692308 | 1.4935760 |
| Female Volleyball | art | 7.692308 | 15.384615 | 76.92308 | 4.384615 | 1.2608503 |
| Female Volleyball | computer | 0.000000 | 30.769231 | 69.23077 | 4.230769 | 0.9268087 |
| Female Volleyball | team_building | 0.000000 | 0.000000 | 100.00000 | 5.000000 | 0.0000000 |
| Female Volleyball | yoga | 23.076923 | 7.692308 | 69.23077 | 4.000000 | 1.4719601 |
| Male Basketball | english | 0.000000 | 33.333333 | 66.66667 | 4.250000 | 0.9653073 |
| Male Basketball | art | 0.000000 | 8.333333 | 91.66667 | 4.750000 | 0.6215816 |
| Male Basketball | computer | 8.333333 | 8.333333 | 83.33333 | 4.250000 | 1.2154311 |
| Male Basketball | team_building | 0.000000 | 0.000000 | 100.00000 | 5.000000 | 0.0000000 |
| Male Basketball | yoga | 0.000000 | 16.666667 | 83.33333 | 4.583333 | 0.7929615 |
| Male Handball | english | 0.000000 | 33.333333 | 66.66667 | 4.166667 | 0.9374369 |
| Male Handball | art | 0.000000 | 8.333333 | 91.66667 | 4.833333 | 0.5773503 |
| Male Handball | computer | 0.000000 | 25.000000 | 75.00000 | 4.500000 | 0.9045340 |
| Male Handball | team_building | 0.000000 | 0.000000 | 100.00000 | 5.000000 | 0.0000000 |
| Male Handball | yoga | 0.000000 | 8.333333 | 91.66667 | 4.833333 | 0.5773503 |
| Male Volleyball | english | 0.000000 | 16.666667 | 83.33333 | 4.500000 | 0.7977240 |
| Male Volleyball | art | 0.000000 | 16.666667 | 83.33333 | 4.583333 | 0.7929615 |
| Male Volleyball | computer | 8.333333 | 16.666667 | 75.00000 | 4.333333 | 1.3026779 |
| Male Volleyball | team_building | 0.000000 | 0.000000 | 100.00000 | 5.000000 | 0.0000000 |
| Male Volleyball | yoga | 8.333333 | 8.333333 | 83.33333 | 4.333333 | 0.9847319 |
plot(likert_subjects_sport_gender, centered=TRUE, center=3, include.center=TRUE)temp_food_sport_gender <- survey_df %>% drop_na(gender, sport) %>% unite("sport group", c(gender, sport), sep = " ") %>% dplyr::select(`sport group`, food) %>% drop_na()
likert_food_sport_gender <- likert(temp_food_sport_gender %>% select(food), grouping = temp_food_sport_gender %>% pull(`sport group`))
likert_food_sport_gender$results| Group | Item | Not Great | Meh | Neutral | Great | Amazing |
|---|---|---|---|---|---|---|
| Female Basketball | food | 0 | 0 | 0.000000 | 0.000000 | 100.00000 |
| Female Handball | food | 0 | 0 | 0.000000 | 0.000000 | 100.00000 |
| Female Volleyball | food | 0 | 0 | 23.076923 | 7.692308 | 69.23077 |
| Male Basketball | food | 0 | 0 | 6.250000 | 6.250000 | 87.50000 |
| Male Handball | food | 0 | 0 | 7.692308 | 0.000000 | 92.30769 |
| Male Volleyball | food | 0 | 0 | 0.000000 | 7.692308 | 92.30769 |
summary(likert_food_sport_gender, center = 3, ordered = TRUE)| Group | Item | low | neutral | high | mean | sd |
|---|---|---|---|---|---|---|
| Female Basketball | food | 0 | 0.000000 | 100.00000 | 5.000000 | 0.0000000 |
| Female Handball | food | 0 | 0.000000 | 100.00000 | 5.000000 | 0.0000000 |
| Female Volleyball | food | 0 | 23.076923 | 76.92308 | 4.461538 | 0.8770580 |
| Male Basketball | food | 0 | 6.250000 | 93.75000 | 4.812500 | 0.5439056 |
| Male Handball | food | 0 | 7.692308 | 92.30769 | 4.846154 | 0.5547002 |
| Male Volleyball | food | 0 | 0.000000 | 100.00000 | 4.923077 | 0.2773501 |
plot(likert_food_sport_gender , centered=TRUE, center=3, include.center=TRUE)temp_overall_sport_gender <- survey_df %>% drop_na(gender, sport) %>% unite("sport group", c(gender, sport), sep = " ") %>% dplyr::select(`sport group`, staff_overall, camp_overall) %>% drop_na()
likert_camp_sport_gender <- likert(temp_overall_sport_gender %>% select(staff_overall, camp_overall), grouping = temp_overall_sport_gender %>% pull(`sport group`))
likert_camp_sport_gender$results| Group | Item | Not Great | Meh | Neutral | Great | Amazing |
|---|---|---|---|---|---|---|
| Female Basketball | staff_overall | 0 | 0 | 0.000000 | 11.111111 | 88.88889 |
| Female Basketball | camp_overall | 0 | 0 | 0.000000 | 0.000000 | 100.00000 |
| Female Handball | staff_overall | 0 | 0 | 8.333333 | 8.333333 | 83.33333 |
| Female Handball | camp_overall | 0 | 0 | 0.000000 | 16.666667 | 83.33333 |
| Female Volleyball | staff_overall | 0 | 0 | 0.000000 | 0.000000 | 100.00000 |
| Female Volleyball | camp_overall | 0 | 0 | 0.000000 | 28.571429 | 71.42857 |
| Male Basketball | staff_overall | 0 | 0 | 6.250000 | 12.500000 | 81.25000 |
| Male Basketball | camp_overall | 0 | 0 | 0.000000 | 0.000000 | 100.00000 |
| Male Handball | staff_overall | 0 | 0 | 0.000000 | 7.692308 | 92.30769 |
| Male Handball | camp_overall | 0 | 0 | 0.000000 | 7.692308 | 92.30769 |
| Male Volleyball | staff_overall | 0 | 0 | 7.692308 | 7.692308 | 84.61538 |
| Male Volleyball | camp_overall | 0 | 0 | 0.000000 | 7.692308 | 92.30769 |
summary(likert_camp_sport_gender, center = 3, ordered = TRUE)| Group | Item | low | neutral | high | mean | sd |
|---|---|---|---|---|---|---|
| Female Basketball | staff_overall | 0 | 0.000000 | 100.00000 | 4.888889 | 0.3333333 |
| Female Basketball | camp_overall | 0 | 0.000000 | 100.00000 | 5.000000 | 0.0000000 |
| Female Handball | staff_overall | 0 | 8.333333 | 91.66667 | 4.750000 | 0.6215816 |
| Female Handball | camp_overall | 0 | 0.000000 | 100.00000 | 4.833333 | 0.3892495 |
| Female Volleyball | staff_overall | 0 | 0.000000 | 100.00000 | 5.000000 | 0.0000000 |
| Female Volleyball | camp_overall | 0 | 0.000000 | 100.00000 | 4.714286 | 0.4688072 |
| Male Basketball | staff_overall | 0 | 6.250000 | 93.75000 | 4.750000 | 0.5773503 |
| Male Basketball | camp_overall | 0 | 0.000000 | 100.00000 | 5.000000 | 0.0000000 |
| Male Handball | staff_overall | 0 | 0.000000 | 100.00000 | 4.923077 | 0.2773501 |
| Male Handball | camp_overall | 0 | 0.000000 | 100.00000 | 4.923077 | 0.2773501 |
| Male Volleyball | staff_overall | 0 | 7.692308 | 92.30769 | 4.769231 | 0.5991447 |
| Male Volleyball | camp_overall | 0 | 0.000000 | 100.00000 | 4.923077 | 0.2773501 |
plot(likert_camp_sport_gender, centered=TRUE, center=3, include.center=TRUE)
Comments
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